MC4: A Tempering Algorithm for Large-sample Network Inference

author: Daniel James Barker, University of Warwick
published: Oct. 14, 2010,   recorded: September 2010,   views: 2795
Categories

Slides

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

Bayesian networks and their variants are widely used for modelling gene regulatory and protein signalling networks. In many settings, it is the underlying network structure itself that is the object of inference. Within a Bayesian framework inferences regarding network structure are made via a posterior probability distribution over graphs. However, in practical problems, the space of graphs is usually too large to permit exact inference, motivating the use of approximate approaches. An MCMC-based algorithm known as MC3 is widely used for network inference in this setting. We argue that recent trends towards larger sample size datasets, while otherwise advantageous, can, for reasons related to concentration of posterior mass, render inference by MC3 harder. We therefore exploit an approach known as parallel tempering to put forward an algorithm for network inference which we call MC4. We show empirical results on both synthetic and proteomic data which highlight the ability of MC4 to converge faster and thereby yield demonstrably accurate results, even in challenging settings where MC3 fails.

See Also:

Download slides icon Download slides: prib2010_barker_mtal_01.pdf (771.9┬áKB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: